re-enable neibs2images while adding it missing parameter. Warn when that we…

theano/sandbox/neighbours.py

@@ -211,21 +211,36 @@ class Images2Neibs(Op):
 def images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
     return Images2Neibs(mode)(ten4, neib_shape, neib_step)
 
-def neibs2images(neibs, neib_shape, original_shape):
+def neibs2images(neibs, neib_shape, original_shape, neib_step=None, mode='valid'):
     """
-    Inverse of images2neib.
-    
-    neibs : matrix like the one obtained by images2neib
-    neib_shape : neib_shape that was used in images2neib
-    original_shape : original shape of the 4d tensor given to images2neib
+    Inverse of images2neib. Don't implement neib_step and mode.
     
+    :type neibs: Theano variable
+    :param neibs: matrix like the one obtained by images2neib
+
+    :type neib_shape: Theano variable
+    :param neib_shape: neib_shape that was used in images2neib
+
+    :type original_shape: Theano variable
+    :param original_shape: original shape of the 4d tensor given to images2neib.
+
+    :type neib_step: Theano variable or None
+    :param neib_step: neib_step that was used in images2neib Implement only None.
+                      None is non overlapping patches and not-adjacent patches.
+
+    :type mode: str
+    :param mode: The mode that was used in images2neib. Implement only valid.
+
     Return a 4d tensor of shape `original_shape`.
     """
     # TODO: handle the case where patches either overlap
     # TODO: handle the case where patches are not directly adjacent
     # TODO: at least separate these cases so that the following code does not incorrectly
     # handle them by accident.
-    raise NotImplementedError('check for overlapping patches or non-adjacent patches.')
+    if neib_step != None:
+        raise NotImplementedError('neibs2images do not implement overlapping patches or non-adjacent patches.')
+    if mode != 'valid':
+        raise NotImplementedError('neibs2images do not implement the mode %s. It currently only implement `valid`.'%mode)
     neibs = T.as_tensor_variable(neibs)
     neib_shape = T.as_tensor_variable(neib_shape)
     original_shape = T.as_tensor_variable(original_shape)